In summary, we train all models with the same simple formula: LSJ (\(1024\times 1024\)  resolution, scale range \([0.1, 2.0]\) ), AdamW [1]} (\(\beta _1, \beta _2 = 0.9, 0.999\) ) with half-period cosine learning rate decay, linear warmup [2]} for \(0.25\)  epochs, and drop path regularization. When using a pre-trained initialization, we fine-tune Mask R-CNN for up to 100 epochs. When training from scratch, we consider schedules of up to 400 epochs since convergence is slower than when using pre-training. We distribute training over 32 or 64 GPUs (NVIDIA V100-32GB) and always use a minibatch size of 64 images. We use PyTorch's automatic mixed precision. Additional hyperparameters are tuned by the consistent application of a protocol, describe next.
